Optimization study of virtual machine placement algorithm based on combined prediction

Shouyan Xing; Haichen Wang; Shiwen Jin

doi:10.1117/12.2671266

29 March 2023 Optimization study of virtual machine placement algorithm based on combined prediction

Shouyan Xing, Haichen Wang, Shiwen Jin

Proceedings Volume 12594, Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022); 1259427 (2023) https://doi.org/10.1117/12.2671266
Event: Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022), 2022, Xi'an, China

Abstract

To address the current problems of high energy consumption and insufficient resource utilization in the scheduling process of cloud virtual machines, an optimization algorithm based on combined prediction for virtual machine placement was proposed. Firstly, a combined prediction model consisting of the BPNN, ARIMA model and LSSVM model was constructed in the load prediction phase to make the detection of overloaded hosts and underloaded hosts more accurate. Secondly, the classical PABFD algorithm was improved in the virtual machine placement phase by adding the constraint of remaining resource utilization of the destination host and proposed an optimal adaptation decreasing algorithm based on resource utilization. Finally, the results of simulation experiments in the CloudSim platform showed that the adoption of this virtual machine placement algorithm can effectively reduce energy consumption while guaranteeing the quality of service.

Citation Download Citation

Shouyan Xing, Haichen Wang, and Shiwen Jin "Optimization study of virtual machine placement algorithm based on combined prediction", Proc. SPIE 12594, Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022), 1259427 (29 March 2023); https://doi.org/10.1117/12.2671266

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KEYWORDS

Data modeling

Clouds

Autoregressive models

Mathematical optimization

Computer simulations

Detection and tracking algorithms

Education and training

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